ew 23(1):

Editorial

Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision

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  • @ARTICLE{10.4108/ew.3450,
        author={Qin Dong},
        title={Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2023},
        month={12},
        keywords={SIFT feature point classification, Fourier series, Harris algorithm, Visual image},
        doi={10.4108/ew.3450}
    }
    
  • Qin Dong
    Year: 2023
    Mobile Robot Vision Image Feature Recognition Method Based on Machine Vision
    EW
    EAI
    DOI: 10.4108/ew.3450
Qin Dong1,*
  • 1: Yancheng Institute of Technology
*Contact email: dong_qin22@163.com

Abstract

In order to improve the efficiency and accuracy of mobile robot visual image feature recognition, a mobile robot visual image feature recognition method based on machine vision is proposed in this paper. Firstly, the development of mobile robot vision is analyzed, and the specific functions of robot visual feature recognition method are designed; Then, the Fourier series method is used to collect the mobile robot visual image, and the matrix associated with the autocorrelation function is calculated according to the Harris algorithm to complete the edge feature extraction of the mobile robot visual image; SIFT feature points of mobile robot visual image are classified, and mobile robot visual image feature recognition is realized through machine vision. The experimental results show that when the number of images is 600, the accuracy of image feature recognition and the loss value of image edge feature extraction of this method are 96.98% and 6.38%, respectively, and the number of iterations is 500. The time of visual image feature recognition of this method is only 3 minutes; The method has the lowest error mean and error variance under different noise conditions. This method can effectively improve the efficiency and accuracy of image feature recognition.